Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+metrics:
+- accuracy
+widget:
+- text: travel book a train ticket
+- text: how much is the average house
+- text: do i need a jacket
+- text: i like the songs of yeshudas please play it
+- text: tell me the current time
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.7743480574773816
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 35 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label                    | Examples                                                                                                                                                                                            |
+|:-------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| alarm_query              | <ul><li>'do i have any alarms set for six am tomorrow'</li><li>'what is the wake up time for my alarm i have set for the flight this weekend'</li><li>'please tell me what alarms are on'</li></ul> |
+| alarm_set                | <ul><li>'set an alarm for six thirty am'</li><li>'add an alarm for tomorrow morning at six am'</li><li>'wake me up at five am'</li></ul>                                                            |
+| audio_volume_mute        | <ul><li>'can you please stop speaking'</li><li>'turn off sound'</li><li>'shut down the sound'</li></ul>                                                                                             |
+| calendar_query           | <ul><li>'how long will my lunch meeting be on tuesday'</li><li>'what time is my doctor appointment on march thirty first'</li><li>'what days do i have booked'</li></ul>                            |
+| calendar_remove          | <ul><li>'clear everything off my calendar for the rest of the year'</li><li>'please clear my calendar'</li><li>'remove from my calendar meeting at nine am'</li></ul>                               |
+| calendar_set             | <ul><li>'new event'</li><li>'remind me of the event in my calendar'</li><li>"mark april twenty as my brother's birthday"</li></ul>                                                                  |
+| cooking_recipe           | <ul><li>'tell me the recipe of'</li><li>'how is rice prepared'</li><li>'what ingredient can be used instead of saffron'</li></ul>                                                                   |
+| datetime_query           | <ul><li>'what is the time in canada now'</li><li>"what's the time in australia"</li><li>'display the local time of london at this moment'</li></ul>                                                 |
+| email_query              | <ul><li>'do i have any unread emails'</li><li>'what about new mail'</li><li>'olly do i have any new emails'</li></ul>                                                                               |
+| email_sendemail          | <ul><li>'dictate email'</li><li>'reply an email to jason that i will not come tonight'</li><li>'please send an email to cassy who is there on my family and friend list'</li></ul>                  |
+| general_quirky           | <ul><li>'where was will ferrell seen last night'</li><li>'do you think i should go to the theater today'</li><li>'what is the best chocolate chip cookies recipe'</li></ul>                         |
+| iot_coffee               | <ul><li>'i need a drink'</li><li>'please activate my coffee pot for me'</li><li>'prepare a cup of coffee for me'</li></ul>                                                                          |
+| iot_hue_lightchange      | <ul><li>'please make the lights natural'</li><li>'make the room light blue'</li><li>'hey olly chance the current light settings'</li></ul>                                                          |
+| iot_hue_lightoff         | <ul><li>'siri please turn the lights off in the bathroom'</li><li>'turn my bedroom lights off'</li><li>'no lights in the kitchen'</li></ul>                                                         |
+| lists_createoradd        | <ul><li>'add business contacts to contact list'</li><li>'please create a new list for me'</li><li>"i want to make this week's shopping list"</li></ul>                                              |
+| lists_query              | <ul><li>'give me all available lists'</li><li>'give me the details on purchase order'</li><li>'find the list'</li></ul>                                                                             |
+| lists_remove             | <ul><li>'replace'</li><li>"delete my to do's for this week"</li><li>'get rid of tax list from nineteen ninety'</li></ul>                                                                            |
+| music_likeness           | <ul><li>'store opinion on song'</li><li>'are there any upcoming concerts by'</li><li>'enter song suggestion'</li></ul>                                                                              |
+| music_query              | <ul><li>'is the song by shakira'</li><li>'which film the music comes from what is the name of the music'</li><li>'which song is this one'</li></ul>                                                 |
+| news_query               | <ul><li>'news articles on a particular subject'</li><li>'get me match highlights'</li><li>'show me the latest news from the guardian'</li></ul>                                                     |
+| play_audiobook           | <ul><li>'continue the last chapter of the audio book i was listening to'</li><li>'open davinci code audiobook'</li><li>'resume the playback of a child called it'</li></ul>                         |
+| play_game                | <ul><li>'bring up papa pear saga'</li><li>'play ping pong'</li><li>'play racing'</li></ul>                                                                                                          |
+| play_music               | <ul><li>'play mf doom anything'</li><li>'play only all music released between the year one thousand nine hundred and ninety and two thousand'</li><li>'nobody knows'</li></ul>                      |
+| play_podcasts            | <ul><li>'play all order of the green hand from previous week'</li><li>'i want to see the next podcast available'</li><li>"search for podcasts that cover men's issues"</li></ul>                    |
+| play_radio               | <ul><li>'can you turn on the radio'</li><li>'play country radio'</li><li>'tune to classic hits'</li></ul>                                                                                           |
+| qa_currency              | <ul><li>'let me know about the exchange rate of rupee to dirham'</li><li>'how much is one dollar in pounds'</li><li>'what is the most current exchange rate in china'</li></ul>                     |
+| qa_definition            | <ul><li>'define elaborate'</li><li>'look up the definition of blunder'</li><li>'give details of rock sand'</li></ul>                                                                                |
+| qa_factoid               | <ul><li>'where are the rocky mountains'</li><li>'what is the population of new york'</li><li>'where is new zealand located on a map'</li></ul>                                                      |
+| recommendation_events    | <ul><li>'are there any fun events in la today'</li><li>"what's happening around me"</li><li>'are there any crafts fairs happening in this area'</li></ul>                                           |
+| recommendation_locations | <ul><li>'what is the nearest pizza shop'</li><li>'please look up local restaurants that are open now'</li><li>'tell me what clothing stores are within five miles of me'</li></ul>                  |
+| social_post              | <ul><li>"tweet at united airlines i'm angry you lost my bags"</li><li>'send a funny message to all of my friends'</li><li>'tweet my current location'</li></ul>                                     |
+| takeaway_query           | <ul><li>'could you please confirm if paradise does takeaway'</li><li>"i've canceled the order placed at mcd did it go through"</li><li>"please find out of charley's steakhouse delivers"</li></ul> |
+| transport_query          | <ul><li>'directions please'</li><li>'what time does the train to place leave'</li><li>'look up the map to stores near me'</li></ul>                                                                 |
+| transport_ticket         | <ul><li>'find me a train ticket to boston'</li><li>'can you please book train tickets for two for this friday'</li><li>'order a train ticket to boston'</li></ul>                                   |
+| weather_query            | <ul><li>'will i need to shovel my driveway this morning'</li><li>'does the weather call for rain saturday'</li><li>'is there any rain in the forecast for the next week'</li></ul>                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7743   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("aisuko/st-mpnet-v2-amazon-mi")
+# Run inference
+preds = model("do i need a jacket")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 1   | 6.7114 | 19  |
+
+| Label                    | Training Sample Count |
+|:-------------------------|:----------------------|
+| alarm_query              | 10                    |
+| alarm_set                | 10                    |
+| audio_volume_mute        | 10                    |
+| calendar_query           | 10                    |
+| calendar_remove          | 10                    |
+| calendar_set             | 10                    |
+| cooking_recipe           | 10                    |
+| datetime_query           | 10                    |
+| email_query              | 10                    |
+| email_sendemail          | 10                    |
+| general_quirky           | 10                    |
+| iot_coffee               | 10                    |
+| iot_hue_lightchange      | 10                    |
+| iot_hue_lightoff         | 10                    |
+| lists_createoradd        | 10                    |
+| lists_query              | 10                    |
+| lists_remove             | 10                    |
+| music_likeness           | 10                    |
+| music_query              | 10                    |
+| news_query               | 10                    |
+| play_audiobook           | 10                    |
+| play_game                | 10                    |
+| play_music               | 10                    |
+| play_podcasts            | 10                    |
+| play_radio               | 10                    |
+| qa_currency              | 10                    |
+| qa_definition            | 10                    |
+| qa_factoid               | 10                    |
+| recommendation_events    | 10                    |
+| recommendation_locations | 10                    |
+| social_post              | 10                    |
+| takeaway_query           | 10                    |
+| transport_query          | 10                    |
+| transport_ticket         | 10                    |
+| weather_query            | 10                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step     | Training Loss | Validation Loss |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0001  | 1        | 0.1814        | -               |
+| 0.0067  | 50       | 0.1542        | -               |
+| 0.0134  | 100      | 0.0953        | -               |
+| 0.0202  | 150      | 0.0991        | -               |
+| 0.0269  | 200      | 0.0717        | -               |
+| 0.0336  | 250      | 0.0653        | -               |
+| 0.0403  | 300      | 0.0412        | -               |
+| 0.0471  | 350      | 0.0534        | -               |
+| 0.0538  | 400      | 0.013         | -               |
+| 0.0605  | 450      | 0.0567        | -               |
+| 0.0672  | 500      | 0.0235        | -               |
+| 0.0739  | 550      | 0.0086        | -               |
+| 0.0807  | 600      | 0.0086        | -               |
+| 0.0874  | 650      | 0.0786        | -               |
+| 0.0941  | 700      | 0.0092        | -               |
+| 0.1008  | 750      | 0.0081        | -               |
+| 0.1076  | 800      | 0.0196        | -               |
+| 0.1143  | 850      | 0.0138        | -               |
+| 0.1210  | 900      | 0.0081        | -               |
+| 0.1277  | 950      | 0.0295        | -               |
+| 0.1344  | 1000     | 0.0074        | -               |
+| 0.1412  | 1050     | 0.0025        | -               |
+| 0.1479  | 1100     | 0.0036        | -               |
+| 0.1546  | 1150     | 0.0021        | -               |
+| 0.1613  | 1200     | 0.0168        | -               |
+| 0.1681  | 1250     | 0.0024        | -               |
+| 0.1748  | 1300     | 0.0039        | -               |
+| 0.1815  | 1350     | 0.0155        | -               |
+| 0.1882  | 1400     | 0.0057        | -               |
+| 0.1949  | 1450     | 0.0027        | -               |
+| 0.2017  | 1500     | 0.0018        | -               |
+| 0.2084  | 1550     | 0.0012        | -               |
+| 0.2151  | 1600     | 0.0032        | -               |
+| 0.2218  | 1650     | 0.0017        | -               |
+| 0.2286  | 1700     | 0.0012        | -               |
+| 0.2353  | 1750     | 0.002         | -               |
+| 0.2420  | 1800     | 0.0025        | -               |
+| 0.2487  | 1850     | 0.0014        | -               |
+| 0.2554  | 1900     | 0.0033        | -               |
+| 0.2622  | 1950     | 0.0007        | -               |
+| 0.2689  | 2000     | 0.0006        | -               |
+| 0.2756  | 2050     | 0.001         | -               |
+| 0.2823  | 2100     | 0.001         | -               |
+| 0.2891  | 2150     | 0.0007        | -               |
+| 0.2958  | 2200     | 0.0011        | -               |
+| 0.3025  | 2250     | 0.0009        | -               |
+| 0.3092  | 2300     | 0.0006        | -               |
+| 0.3159  | 2350     | 0.001         | -               |
+| 0.3227  | 2400     | 0.0005        | -               |
+| 0.3294  | 2450     | 0.0012        | -               |
+| 0.3361  | 2500     | 0.0005        | -               |
+| 0.3428  | 2550     | 0.0007        | -               |
+| 0.3496  | 2600     | 0.0018        | -               |
+| 0.3563  | 2650     | 0.0008        | -               |
+| 0.3630  | 2700     | 0.0009        | -               |
+| 0.3697  | 2750     | 0.0007        | -               |
+| 0.3764  | 2800     | 0.0013        | -               |
+| 0.3832  | 2850     | 0.0004        | -               |
+| 0.3899  | 2900     | 0.0005        | -               |
+| 0.3966  | 2950     | 0.0005        | -               |
+| 0.4033  | 3000     | 0.0006        | -               |
+| 0.4101  | 3050     | 0.0005        | -               |
+| 0.4168  | 3100     | 0.0004        | -               |
+| 0.4235  | 3150     | 0.0007        | -               |
+| 0.4302  | 3200     | 0.0009        | -               |
+| 0.4369  | 3250     | 0.0007        | -               |
+| 0.4437  | 3300     | 0.0007        | -               |
+| 0.4504  | 3350     | 0.0004        | -               |
+| 0.4571  | 3400     | 0.0004        | -               |
+| 0.4638  | 3450     | 0.0009        | -               |
+| 0.4706  | 3500     | 0.0006        | -               |
+| 0.4773  | 3550     | 0.0006        | -               |
+| 0.4840  | 3600     | 0.0005        | -               |
+| 0.4907  | 3650     | 0.0005        | -               |
+| 0.4974  | 3700     | 0.0003        | -               |
+| 0.5042  | 3750     | 0.0004        | -               |
+| 0.5109  | 3800     | 0.0004        | -               |
+| 0.5176  | 3850     | 0.0005        | -               |
+| 0.5243  | 3900     | 0.0007        | -               |
+| 0.5311  | 3950     | 0.0005        | -               |
+| 0.5378  | 4000     | 0.0006        | -               |
+| 0.5445  | 4050     | 0.0004        | -               |
+| 0.5512  | 4100     | 0.0006        | -               |
+| 0.5579  | 4150     | 0.0005        | -               |
+| 0.5647  | 4200     | 0.0004        | -               |
+| 0.5714  | 4250     | 0.0003        | -               |
+| 0.5781  | 4300     | 0.0003        | -               |
+| 0.5848  | 4350     | 0.0005        | -               |
+| 0.5916  | 4400     | 0.0002        | -               |
+| 0.5983  | 4450     | 0.0006        | -               |
+| 0.6050  | 4500     | 0.0004        | -               |
+| 0.6117  | 4550     | 0.0005        | -               |
+| 0.6184  | 4600     | 0.0003        | -               |
+| 0.6252  | 4650     | 0.0005        | -               |
+| 0.6319  | 4700     | 0.0007        | -               |
+| 0.6386  | 4750     | 0.0003        | -               |
+| 0.6453  | 4800     | 0.0004        | -               |
+| 0.6521  | 4850     | 0.0004        | -               |
+| 0.6588  | 4900     | 0.0004        | -               |
+| 0.6655  | 4950     | 0.0003        | -               |
+| 0.6722  | 5000     | 0.0003        | -               |
+| 0.6789  | 5050     | 0.0004        | -               |
+| 0.6857  | 5100     | 0.0003        | -               |
+| 0.6924  | 5150     | 0.0005        | -               |
+| 0.6991  | 5200     | 0.0002        | -               |
+| 0.7058  | 5250     | 0.0004        | -               |
+| 0.7126  | 5300     | 0.0003        | -               |
+| 0.7193  | 5350     | 0.0007        | -               |
+| 0.7260  | 5400     | 0.0002        | -               |
+| 0.7327  | 5450     | 0.0002        | -               |
+| 0.7394  | 5500     | 0.0005        | -               |
+| 0.7462  | 5550     | 0.0003        | -               |
+| 0.7529  | 5600     | 0.0003        | -               |
+| 0.7596  | 5650     | 0.0003        | -               |
+| 0.7663  | 5700     | 0.0004        | -               |
+| 0.7731  | 5750     | 0.0004        | -               |
+| 0.7798  | 5800     | 0.0004        | -               |
+| 0.7865  | 5850     | 0.0003        | -               |
+| 0.7932  | 5900     | 0.0003        | -               |
+| 0.7999  | 5950     | 0.0004        | -               |
+| 0.8067  | 6000     | 0.0004        | -               |
+| 0.8134  | 6050     | 0.0004        | -               |
+| 0.8201  | 6100     | 0.0003        | -               |
+| 0.8268  | 6150     | 0.0002        | -               |
+| 0.8336  | 6200     | 0.0005        | -               |
+| 0.8403  | 6250     | 0.0003        | -               |
+| 0.8470  | 6300     | 0.0003        | -               |
+| 0.8537  | 6350     | 0.0002        | -               |
+| 0.8604  | 6400     | 0.0003        | -               |
+| 0.8672  | 6450     | 0.0004        | -               |
+| 0.8739  | 6500     | 0.0002        | -               |
+| 0.8806  | 6550     | 0.0003        | -               |
+| 0.8873  | 6600     | 0.0003        | -               |
+| 0.8941  | 6650     | 0.0002        | -               |
+| 0.9008  | 6700     | 0.0002        | -               |
+| 0.9075  | 6750     | 0.0002        | -               |
+| 0.9142  | 6800     | 0.0002        | -               |
+| 0.9209  | 6850     | 0.0003        | -               |
+| 0.9277  | 6900     | 0.0002        | -               |
+| 0.9344  | 6950     | 0.0002        | -               |
+| 0.9411  | 7000     | 0.0002        | -               |
+| 0.9478  | 7050     | 0.0002        | -               |
+| 0.9546  | 7100     | 0.0002        | -               |
+| 0.9613  | 7150     | 0.0003        | -               |
+| 0.9680  | 7200     | 0.0002        | -               |
+| 0.9747  | 7250     | 0.0003        | -               |
+| 0.9814  | 7300     | 0.0002        | -               |
+| 0.9882  | 7350     | 0.0003        | -               |
+| 0.9949  | 7400     | 0.0003        | -               |
+| **1.0** | **7438** | **-**         | **0.0755**      |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.39.3
+- PyTorch: 2.1.2
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "checkpoints/step_7438",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,9 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

@@ -0,0 +1,40 @@
+{
+  "labels": [
+    "alarm_query",
+    "alarm_set",
+    "audio_volume_mute",
+    "calendar_query",
+    "calendar_remove",
+    "calendar_set",
+    "cooking_recipe",
+    "datetime_query",
+    "email_query",
+    "email_sendemail",
+    "general_quirky",
+    "iot_coffee",
+    "iot_hue_lightchange",
+    "iot_hue_lightoff",
+    "lists_createoradd",
+    "lists_query",
+    "lists_remove",
+    "music_likeness",
+    "music_query",
+    "news_query",
+    "play_audiobook",
+    "play_game",
+    "play_music",
+    "play_podcasts",
+    "play_radio",
+    "qa_currency",
+    "qa_definition",
+    "qa_factoid",
+    "recommendation_events",
+    "recommendation_locations",
+    "social_post",
+    "takeaway_query",
+    "transport_query",
+    "transport_ticket",
+    "weather_query"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b482e150831172528ea843eb4d7551be91ddd15f31e94cffb99ae7739b537f0b
+size 437967672

model_head.pkl

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+oid sha256:1e5fd14bfcf391c3d5c0c7e2c6a3d76901b1146b5cdd3a4095e899784c463db5
+size 219519

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
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+      "special": true
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": true,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}